Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f9d03e70cc0>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f9d005e0860>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
import numpy as np

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.2.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, [None,image_width,image_height,image_channels], name='Real_Input')
    inputs_z = tf.placeholder(tf.float32, [None, z_dim], name='Z_Input')
    learning_rate = tf.placeholder(tf.float32, name='Learning_Rate')
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/paperspace/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/home/paperspace/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-8a0c96918b83>", line 22, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/paperspace/dl/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/paperspace/dl/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/paperspace/dl/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/paperspace/dl/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/paperspace/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    keep_prob = 0.5
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha=0.15
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)

        x3 = tf.layers.conv2d(relu1, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)

        
        flat = tf.reshape(relu3, (-1, 7*7*256))
        flat = tf.nn.dropout(flat, keep_prob)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAoutT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse = not is_train):
        alpha=0.15  
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*256)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256 now

        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 28x28x128 now
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')

        # 28x28x5 now
        out = tf.tanh(logits)
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    

    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    
    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    image_channels=1
    if data_image_mode =="RGB":
        image_channels = 3


    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], image_channels, 200)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)

   

    sample_z = np.random.uniform(-1, 1, size=(72, z_dim))

    samples, losses = [], []
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps+=1
                # batch images
                batch_images = batch_images.reshape(batch_size, data_shape[1], data_shape[2], image_channels)
                batch_images = batch_images*2

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z,lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images,lr:learning_rate})

                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                if steps % 100 == 0:
                    show_generator_output(sess=sess, n_images=30, input_z=input_z, out_channel_dim=image_channels, image_mode=data_image_mode)


      # saver.save(sess, './checkpoints/generator.ckpt')

    #with open('samples.pkl', 'wb') as f:
     #   pkl.dump(samples, f)

    return losses, samples

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 16
z_dim = 200
learning_rate = 0.0004
beta1 = 0.3




"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 0.1222... Generator Loss: 3.0906
Epoch 1/2... Discriminator Loss: 0.0222... Generator Loss: 5.1858
Epoch 1/2... Discriminator Loss: 0.0117... Generator Loss: 5.2396
Epoch 1/2... Discriminator Loss: 0.0820... Generator Loss: 3.3636
Epoch 1/2... Discriminator Loss: 0.0504... Generator Loss: 4.5476
Epoch 1/2... Discriminator Loss: 0.0386... Generator Loss: 4.5573
Epoch 1/2... Discriminator Loss: 0.0060... Generator Loss: 5.5678
Epoch 1/2... Discriminator Loss: 0.2606... Generator Loss: 2.3065
Epoch 1/2... Discriminator Loss: 2.6003... Generator Loss: 0.4251
Epoch 1/2... Discriminator Loss: 2.7274... Generator Loss: 0.1897

Epoch 1/2... Discriminator Loss: 2.7507... Generator Loss: 0.3926
Epoch 1/2... Discriminator Loss: 2.3608... Generator Loss: 0.4293
Epoch 1/2... Discriminator Loss: 1.3062... Generator Loss: 1.1801
Epoch 1/2... Discriminator Loss: 3.2072... Generator Loss: 0.1502
Epoch 1/2... Discriminator Loss: 1.6752... Generator Loss: 0.5966
Epoch 1/2... Discriminator Loss: 2.0462... Generator Loss: 0.2473
Epoch 1/2... Discriminator Loss: 1.6125... Generator Loss: 0.7847
Epoch 1/2... Discriminator Loss: 1.5153... Generator Loss: 0.5130
Epoch 1/2... Discriminator Loss: 2.8280... Generator Loss: 0.2945
Epoch 1/2... Discriminator Loss: 1.9440... Generator Loss: 0.1273

Epoch 1/2... Discriminator Loss: 2.1909... Generator Loss: 0.4823
Epoch 1/2... Discriminator Loss: 2.3760... Generator Loss: 0.2444
Epoch 1/2... Discriminator Loss: 1.9237... Generator Loss: 0.9305
Epoch 1/2... Discriminator Loss: 1.8618... Generator Loss: 0.5068
Epoch 1/2... Discriminator Loss: 1.5443... Generator Loss: 0.7435
Epoch 1/2... Discriminator Loss: 1.8798... Generator Loss: 0.2694
Epoch 1/2... Discriminator Loss: 2.0133... Generator Loss: 0.2340
Epoch 1/2... Discriminator Loss: 2.1651... Generator Loss: 0.3392
Epoch 1/2... Discriminator Loss: 2.3156... Generator Loss: 0.4865
Epoch 1/2... Discriminator Loss: 2.2964... Generator Loss: 0.2255

Epoch 1/2... Discriminator Loss: 1.5123... Generator Loss: 0.7602
Epoch 1/2... Discriminator Loss: 1.4106... Generator Loss: 0.4584
Epoch 1/2... Discriminator Loss: 1.9574... Generator Loss: 0.5426
Epoch 1/2... Discriminator Loss: 1.7184... Generator Loss: 1.0236
Epoch 1/2... Discriminator Loss: 1.7564... Generator Loss: 1.2638
Epoch 1/2... Discriminator Loss: 1.7152... Generator Loss: 0.4461
Epoch 1/2... Discriminator Loss: 1.7497... Generator Loss: 0.8893
Epoch 1/2... Discriminator Loss: 1.3564... Generator Loss: 0.4331
Epoch 1/2... Discriminator Loss: 2.3187... Generator Loss: 0.2790
Epoch 1/2... Discriminator Loss: 1.7480... Generator Loss: 0.3404

Epoch 1/2... Discriminator Loss: 1.6833... Generator Loss: 0.3835
Epoch 1/2... Discriminator Loss: 1.6079... Generator Loss: 0.4137
Epoch 1/2... Discriminator Loss: 1.4508... Generator Loss: 1.5897
Epoch 1/2... Discriminator Loss: 1.5357... Generator Loss: 0.4683
Epoch 1/2... Discriminator Loss: 1.6597... Generator Loss: 1.7705
Epoch 1/2... Discriminator Loss: 1.2788... Generator Loss: 1.1255
Epoch 1/2... Discriminator Loss: 2.2906... Generator Loss: 0.3550
Epoch 1/2... Discriminator Loss: 1.7830... Generator Loss: 0.3833
Epoch 1/2... Discriminator Loss: 1.4311... Generator Loss: 0.4033
Epoch 1/2... Discriminator Loss: 1.0634... Generator Loss: 0.3815

Epoch 1/2... Discriminator Loss: 2.1576... Generator Loss: 0.5405
Epoch 1/2... Discriminator Loss: 2.0439... Generator Loss: 0.3448
Epoch 1/2... Discriminator Loss: 1.3412... Generator Loss: 0.9410
Epoch 1/2... Discriminator Loss: 1.7084... Generator Loss: 0.4412
Epoch 1/2... Discriminator Loss: 1.9174... Generator Loss: 0.6769
Epoch 1/2... Discriminator Loss: 1.3675... Generator Loss: 1.1123
Epoch 1/2... Discriminator Loss: 1.4956... Generator Loss: 0.5810
Epoch 1/2... Discriminator Loss: 1.9100... Generator Loss: 0.4591
Epoch 1/2... Discriminator Loss: 1.7728... Generator Loss: 1.3027
Epoch 1/2... Discriminator Loss: 2.5426... Generator Loss: 0.3416

Epoch 1/2... Discriminator Loss: 1.7615... Generator Loss: 0.6704
Epoch 1/2... Discriminator Loss: 2.0470... Generator Loss: 0.7882
Epoch 1/2... Discriminator Loss: 1.5974... Generator Loss: 0.5371
Epoch 1/2... Discriminator Loss: 2.0845... Generator Loss: 0.5263
Epoch 1/2... Discriminator Loss: 1.1508... Generator Loss: 0.6583
Epoch 1/2... Discriminator Loss: 1.3656... Generator Loss: 1.6006
Epoch 1/2... Discriminator Loss: 1.8306... Generator Loss: 0.2497
Epoch 1/2... Discriminator Loss: 1.8483... Generator Loss: 1.2113
Epoch 1/2... Discriminator Loss: 1.6374... Generator Loss: 0.4549
Epoch 1/2... Discriminator Loss: 1.9737... Generator Loss: 0.6189

Epoch 1/2... Discriminator Loss: 1.4521... Generator Loss: 0.8726
Epoch 1/2... Discriminator Loss: 2.0409... Generator Loss: 0.4269
Epoch 1/2... Discriminator Loss: 1.3717... Generator Loss: 0.3001
Epoch 1/2... Discriminator Loss: 1.2681... Generator Loss: 1.6651
Epoch 1/2... Discriminator Loss: 2.2207... Generator Loss: 0.2144
Epoch 1/2... Discriminator Loss: 1.4356... Generator Loss: 0.7934
Epoch 1/2... Discriminator Loss: 1.3713... Generator Loss: 1.2424
Epoch 1/2... Discriminator Loss: 1.9437... Generator Loss: 0.5913
Epoch 1/2... Discriminator Loss: 1.9642... Generator Loss: 0.3870
Epoch 1/2... Discriminator Loss: 2.2054... Generator Loss: 0.4483

Epoch 1/2... Discriminator Loss: 1.7783... Generator Loss: 0.5773
Epoch 1/2... Discriminator Loss: 1.2623... Generator Loss: 1.0235
Epoch 1/2... Discriminator Loss: 2.4015... Generator Loss: 0.3020
Epoch 1/2... Discriminator Loss: 1.8206... Generator Loss: 1.4036
Epoch 1/2... Discriminator Loss: 2.1196... Generator Loss: 0.3345
Epoch 1/2... Discriminator Loss: 1.4265... Generator Loss: 1.1632
Epoch 1/2... Discriminator Loss: 1.6751... Generator Loss: 0.8643
Epoch 1/2... Discriminator Loss: 2.0352... Generator Loss: 1.0858
Epoch 1/2... Discriminator Loss: 1.7475... Generator Loss: 0.4933
Epoch 1/2... Discriminator Loss: 1.7635... Generator Loss: 0.6522

Epoch 1/2... Discriminator Loss: 1.9606... Generator Loss: 0.5261
Epoch 1/2... Discriminator Loss: 1.4393... Generator Loss: 0.3791
Epoch 1/2... Discriminator Loss: 1.4912... Generator Loss: 1.2660
Epoch 1/2... Discriminator Loss: 1.2259... Generator Loss: 0.8225
Epoch 1/2... Discriminator Loss: 1.8557... Generator Loss: 0.2465
Epoch 1/2... Discriminator Loss: 1.2358... Generator Loss: 0.5348
Epoch 1/2... Discriminator Loss: 2.1348... Generator Loss: 0.9605
Epoch 1/2... Discriminator Loss: 1.5644... Generator Loss: 0.6882
Epoch 1/2... Discriminator Loss: 1.1562... Generator Loss: 0.8958
Epoch 1/2... Discriminator Loss: 1.6417... Generator Loss: 1.5310

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 16
z_dim = 200s
learning_rate = 0.0004
beta1 = 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/1... Discriminator Loss: 0.7069... Generator Loss: 1.1531
Epoch 1/1... Discriminator Loss: 0.7951... Generator Loss: 0.8916
Epoch 1/1... Discriminator Loss: 0.7303... Generator Loss: 0.8577
Epoch 1/1... Discriminator Loss: 2.2635... Generator Loss: 0.5144
Epoch 1/1... Discriminator Loss: 0.8500... Generator Loss: 2.2804
Epoch 1/1... Discriminator Loss: 2.1193... Generator Loss: 0.8660
Epoch 1/1... Discriminator Loss: 0.6939... Generator Loss: 1.6465
Epoch 1/1... Discriminator Loss: 1.2472... Generator Loss: 0.9753
Epoch 1/1... Discriminator Loss: 1.0891... Generator Loss: 1.6303
Epoch 1/1... Discriminator Loss: 0.4138... Generator Loss: 2.1883

Epoch 1/1... Discriminator Loss: 1.5577... Generator Loss: 0.8011
Epoch 1/1... Discriminator Loss: 0.4269... Generator Loss: 1.2418
Epoch 1/1... Discriminator Loss: 0.2844... Generator Loss: 1.9195
Epoch 1/1... Discriminator Loss: 0.9292... Generator Loss: 0.6377
Epoch 1/1... Discriminator Loss: 0.2542... Generator Loss: 2.6957
Epoch 1/1... Discriminator Loss: 0.2665... Generator Loss: 3.3088
Epoch 1/1... Discriminator Loss: 1.7169... Generator Loss: 6.3266
Epoch 1/1... Discriminator Loss: 1.3464... Generator Loss: 0.7209
Epoch 1/1... Discriminator Loss: 0.9743... Generator Loss: 4.5103
Epoch 1/1... Discriminator Loss: 0.6924... Generator Loss: 1.8903

Epoch 1/1... Discriminator Loss: 1.0935... Generator Loss: 1.0445
Epoch 1/1... Discriminator Loss: 2.8041... Generator Loss: 0.0665
Epoch 1/1... Discriminator Loss: 0.9735... Generator Loss: 0.3251
Epoch 1/1... Discriminator Loss: 2.1347... Generator Loss: 0.1654
Epoch 1/1... Discriminator Loss: 0.6255... Generator Loss: 1.7213
Epoch 1/1... Discriminator Loss: 0.4680... Generator Loss: 1.8093
Epoch 1/1... Discriminator Loss: 0.9308... Generator Loss: 0.8910
Epoch 1/1... Discriminator Loss: 0.6147... Generator Loss: 1.3983
Epoch 1/1... Discriminator Loss: 0.7936... Generator Loss: 3.3883
Epoch 1/1... Discriminator Loss: 2.5716... Generator Loss: 0.0941

Epoch 1/1... Discriminator Loss: 1.5846... Generator Loss: 1.2020
Epoch 1/1... Discriminator Loss: 3.7888... Generator Loss: 0.0522
Epoch 1/1... Discriminator Loss: 1.0568... Generator Loss: 0.7839
Epoch 1/1... Discriminator Loss: 1.8918... Generator Loss: 0.4028
Epoch 1/1... Discriminator Loss: 1.6978... Generator Loss: 0.4742
Epoch 1/1... Discriminator Loss: 1.4825... Generator Loss: 0.1991
Epoch 1/1... Discriminator Loss: 0.2963... Generator Loss: 3.2081
Epoch 1/1... Discriminator Loss: 1.8335... Generator Loss: 0.6809
Epoch 1/1... Discriminator Loss: 1.0050... Generator Loss: 1.2772
Epoch 1/1... Discriminator Loss: 1.3896... Generator Loss: 4.3886

Epoch 1/1... Discriminator Loss: 1.5878... Generator Loss: 0.3625
Epoch 1/1... Discriminator Loss: 0.5416... Generator Loss: 1.3683
Epoch 1/1... Discriminator Loss: 2.2994... Generator Loss: 1.2406
Epoch 1/1... Discriminator Loss: 2.0429... Generator Loss: 0.6210
Epoch 1/1... Discriminator Loss: 1.6037... Generator Loss: 0.3087
Epoch 1/1... Discriminator Loss: 0.2349... Generator Loss: 4.6031
Epoch 1/1... Discriminator Loss: 1.3775... Generator Loss: 0.5211
Epoch 1/1... Discriminator Loss: 1.4659... Generator Loss: 0.6971
Epoch 1/1... Discriminator Loss: 0.7208... Generator Loss: 1.3624
Epoch 1/1... Discriminator Loss: 0.5793... Generator Loss: 2.1330

Epoch 1/1... Discriminator Loss: 2.8966... Generator Loss: 0.1627
Epoch 1/1... Discriminator Loss: 1.4245... Generator Loss: 0.4367
Epoch 1/1... Discriminator Loss: 0.5928... Generator Loss: 3.5942
Epoch 1/1... Discriminator Loss: 2.1024... Generator Loss: 1.4205
Epoch 1/1... Discriminator Loss: 1.0755... Generator Loss: 1.6205
Epoch 1/1... Discriminator Loss: 0.3622... Generator Loss: 7.8700
Epoch 1/1... Discriminator Loss: 1.1783... Generator Loss: 2.0974
Epoch 1/1... Discriminator Loss: 1.8546... Generator Loss: 0.3511
Epoch 1/1... Discriminator Loss: 1.9484... Generator Loss: 0.4403
Epoch 1/1... Discriminator Loss: 1.4404... Generator Loss: 0.5724

Epoch 1/1... Discriminator Loss: 0.7079... Generator Loss: 0.9763
Epoch 1/1... Discriminator Loss: 0.9479... Generator Loss: 4.9360
Epoch 1/1... Discriminator Loss: 0.6830... Generator Loss: 1.2915
Epoch 1/1... Discriminator Loss: 0.5139... Generator Loss: 2.6160
Epoch 1/1... Discriminator Loss: 0.3202... Generator Loss: 2.3906
Epoch 1/1... Discriminator Loss: 1.5501... Generator Loss: 0.6967
Epoch 1/1... Discriminator Loss: 1.0820... Generator Loss: 1.0617
Epoch 1/1... Discriminator Loss: 0.9206... Generator Loss: 2.3249
Epoch 1/1... Discriminator Loss: 1.9567... Generator Loss: 0.2688
Epoch 1/1... Discriminator Loss: 0.3977... Generator Loss: 3.2202

Epoch 1/1... Discriminator Loss: 2.2075... Generator Loss: 0.3248
Epoch 1/1... Discriminator Loss: 0.6676... Generator Loss: 2.0615
Epoch 1/1... Discriminator Loss: 0.9132... Generator Loss: 1.5735
Epoch 1/1... Discriminator Loss: 1.2061... Generator Loss: 0.7819
Epoch 1/1... Discriminator Loss: 1.1156... Generator Loss: 2.2510
Epoch 1/1... Discriminator Loss: 1.8252... Generator Loss: 0.3995
Epoch 1/1... Discriminator Loss: 0.8192... Generator Loss: 2.5977
Epoch 1/1... Discriminator Loss: 1.3456... Generator Loss: 0.6566
Epoch 1/1... Discriminator Loss: 4.4842... Generator Loss: 3.5030
Epoch 1/1... Discriminator Loss: 2.0783... Generator Loss: 0.4075

Epoch 1/1... Discriminator Loss: 1.5203... Generator Loss: 0.8341
Epoch 1/1... Discriminator Loss: 0.9245... Generator Loss: 1.7061
Epoch 1/1... Discriminator Loss: 2.9141... Generator Loss: 0.0797
Epoch 1/1... Discriminator Loss: 0.8061... Generator Loss: 1.2324
Epoch 1/1... Discriminator Loss: 1.4637... Generator Loss: 0.6929
Epoch 1/1... Discriminator Loss: 1.5076... Generator Loss: 0.2472
Epoch 1/1... Discriminator Loss: 0.7205... Generator Loss: 1.9302
Epoch 1/1... Discriminator Loss: 1.0311... Generator Loss: 0.6384
Epoch 1/1... Discriminator Loss: 0.2258... Generator Loss: 2.2644
Epoch 1/1... Discriminator Loss: 2.3999... Generator Loss: 0.2551

Epoch 1/1... Discriminator Loss: 1.0231... Generator Loss: 1.2133
Epoch 1/1... Discriminator Loss: 0.5799... Generator Loss: 1.5963
Epoch 1/1... Discriminator Loss: 1.1368... Generator Loss: 1.0321
Epoch 1/1... Discriminator Loss: 0.7408... Generator Loss: 1.2475
Epoch 1/1... Discriminator Loss: 2.1635... Generator Loss: 0.5108
Epoch 1/1... Discriminator Loss: 0.9147... Generator Loss: 2.1903
Epoch 1/1... Discriminator Loss: 0.9186... Generator Loss: 0.7412
Epoch 1/1... Discriminator Loss: 1.9345... Generator Loss: 0.5500
Epoch 1/1... Discriminator Loss: 1.6590... Generator Loss: 0.7214
Epoch 1/1... Discriminator Loss: 2.2768... Generator Loss: 0.1726

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.